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Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection.
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Oded Maler and Dejan Nickovic · 2004
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Numerical Optimization
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The security of machine learning
Marco Barreno, Blaine Nelson, Anthony D Joseph, and JD Tygar · 2010
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Large-scale malware classification using random projections and neural networks
George E Dahl, Jack W Stokes, Li Deng, and Dong Yu · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Mining requirements from closed-loop control models
Xiaoqing Jin, Alexandre Donzé, Jyotirmoy Deshmukh, and Sanjit A. Seshia · 2015
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How paypal beats the bad guys with machine learning
Eric Knorr · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi et al · 2015
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Distributional smoothing by virtual adversarial examples
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2015
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Nvidia tegra drive px: Self-driving car computer, 2015
NVIDIA · 2015
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Letter to the editor: Research priorities for robust and beneficial artif icial intelligence: An open letter
Stuart Russell, Tom Dietterich, Eric Horvitz, Bart Selman, Francesca Rossi, Demis Hassabis, Shane Legg, Mustafa Suleyman, Dileep George, and Scott Phoenix · 2015
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicolas Carlini and David Wagner · 2017
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Compositional falsification of cyber-physical systems with machine learning components
Tommaso Dreossi, Alexandre Donzé, and Sanjit A. Seshia · 2017
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Compositional falsification of cyber-physical systems with machine learning components
Tommaso Dreossi, Alexandre Donzé, and Sanjit A. Seshia · 2017
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Safety verification of deep neural networks
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
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Recognizing functions in binaries with neural networks
Eui Chul Richard Shin, Dawn Song, and Reza Moazzezi · 2015
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End to end learning for self-driving cars
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba · 2016
Cited alongside, same era.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba · 2016
Cited alongside, same era.
Ai, machine learning drive autonomous vehicle development
Nathan Eddy · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Policy compression for aircraft collision avoidance systems
K. Julian, J. Lopez, J. Brush, M. Owen, and M. Kochenderfer · 2016
Cited alongside, same era.
Introduction to Embedded Systems: A Cyber-Physical Systems Approach
Edward A. Lee and Sanjit A. Seshia · 2016
Cited alongside, same era.
Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer · 2017
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
J. Zico Kolter and Eric Wong · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z.Berkay Celik, and Ananthram Swami · 2017
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Compositional verification without compositional specification for learning-based systems
Sanjit A. Seshia · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei Koh, and Percy Liang · 2017
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Counterexample-guided data augmentation
Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, and Sanjit A. Seshia · 2018
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Output range analysis for deep neural networks
Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan, and Ashish Tiwari · 2018
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A Dual Approach to Scalable Verification of Deep Networks
K. Dvijotham, R. Stanforth, S. Gowal, T. Mann, and P. Kohli · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Mądry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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